Comparison of Vibration Signal Analysis Methods for Effective Gear Fault Detection

2014 ◽  
Vol 709 ◽  
pp. 456-459
Author(s):  
Woong Yong Lee ◽  
Dong Hyong Lee ◽  
Hae Young Ji
Keyword(s):  
Signal Analysis ◽  
High Speed ◽  
Vibration Signal ◽  
Spur Gear ◽  
High Speed Train ◽  
Crest Factor ◽  
Gear Fault ◽  
Specimen Test ◽  
Vibration Signal Analysis ◽  
Crack Condition

Reduction unit for high-speed train is an important component. However if faults of reduction unit occurred, the damages such as material and human damage have been caused. To prevent the damage, it is necessary to study reduction unit monitoring for high-speed train. We conducted spur gear specimen test which was crack, breakage and pitting tests and analyzed FFT, Sideband energy ratio (SER), RMS, crest factor, and kurtosis. There was not distinct difference between no-fault and pitting condition at RMS, crest factor and kurtosis. But SER increased depending on crack condition. In breakage test, all parameters had difference between no-fault and breakage condition.

A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling

2007 ◽  
Vol 345-346 ◽  
pp. 1303-1306
Author(s):  
Bum Won Bae ◽  
In Pil Kang ◽  
Yeon Sun Choi
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Detection Method ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Vibration Signal Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

Slow Speed Vibration Signal Analysis: If You Can’t Do It Slow, You Can’t Do It Fast

2007 ◽  
Author(s):  
Mel G. Maalouf
Keyword(s):  
Signal Analysis ◽  
High Speed ◽  
Vibration Signal ◽  
Slow Motion ◽  
Slow Speed ◽  
Response Characteristics ◽  
Slow Roll ◽  
Full Speed ◽  
Vibration Signal Analysis ◽  
Run Up

For many people, the interpretation of vibration signals for a machine at running speed is complicated and foreign, and is considered an art in many circles. Interpreting the rich characteristics of the raw signals during run-up and coast down requires even more skill and experience. For some, interpreting the signals at slow speeds (sometimes called slow roll speeds) is so difficult that the signals are often ignored and discredited as not useful data. This paper will communicate the author’s experience in using this valuable, yet sometimes difficult, data to correlate and corroborate with high-speed data. This data and interpretation are used to understand the dynamic behavior of the machine while the forces on the rotor are driving the response characteristics at run up, full speed and coast down. In the sports arena, good coaches often say that if you cannot execute skills in slow motion, you likely won’t be able to execute them at normal speeds and absolutely not in high-pressure game situations. This is also true for vibration diagnostics: if you don’t do correct slow speed analysis, full speed and transient (startup and coast down) analysis may be misleading or just wrong. In this instance, the analyses and diagnostic calls that were made by using slow speed signal analysis include: Selecting Slow Roll Values, Shaft Surface Quality, Direction of Rotation, Rotor Bow (Gravity), Rotor Bow (Thermal), Locked up Coupling, Non-Concentric Coupling, Reverse Rotation. This paper will describe the methodologies for collecting data and the analysis of the data to make the above calls on specific examples experienced by the author and his colleagues.

High Speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature

2013 ◽  
Vol 753-755 ◽  
pp. 2286-2289 ◽  
Author(s):  
Na Qin ◽  
Wei Dong Jin ◽  
Jin Huang ◽  
Peng Jiang ◽  
Zhi Min Li
Keyword(s):  
Signal Analysis ◽  
High Speed ◽  
Performance Monitoring ◽  
Vibration Signal ◽  
High Speed Train ◽  
Wavelet Entropy ◽  
Result Show ◽  
High Speed Trains ◽  
Dimension Space ◽  
Train System

Mechanical behavior of high speed trains bogie seriously impact the reliability of the train system. Performance monitoring and fault diagnosis for the critical component on bogie are very important. Simulation data of high speed train bogie fault signal is selected in data experiment. Based on multiresolution analysis, wavelet entropy features are extracted to reflect the uncertainty level of the vibration signal on scales. In the high dimension space composed by several wavelet entropy features, the dates from four fault patterns are classified and the result is satisfactory. Result show that wavelet entropy feature is effective for fault signal analysis of high speed train bogie.

scholarly journals Research on transformer condition evaluation technology based on vibration signal analysis

2021 ◽  
Vol 645 ◽  
pp. 012036
Author(s):  
Ma Hao ◽  
Yao Chuang ◽  
Duan Minghui ◽  
Wei Jufang ◽  
Zhang Xin ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Condition Evaluation ◽  
Vibration Signal Analysis

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

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